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2018 | OriginalPaper | Chapter

Weighted Fuzzy KNN Optimized by Simulated Annealing for Classification of Large Data: A New Approach to Skin Detection

Authors : Swati Aggarwal, Lehar Bhandari, Karan Kapoor, Jaswin Kaur

Published in: Data Science and Analytics

Publisher: Springer Singapore

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Abstract

Machine learning is being used in every field. In almost all technical and financial domains, machine learning is being used enormously from predicting new outcomes to classifying a given data into multiple sets. In this research work it has been tried to build and expand upon previously built binary classifiers to develop a unique classifier for skin detection that separates the given input data into two sets – Skin segment and Non-Skin segment. Skin Detection essentially means detecting in an image or video pixels or regions which are of the skin color. The input data given to the classifier has three attributes - value of the red, green and blue channel. The combination of these three values is the color of the object seen. The classifier classifies the input data into the above two classes on the basis of these attributes. In general, this classifier can be extended to any binary class data.

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previous chapter A Survey of Techniques Used in Processing and Mining of Medical Images
next chapter Comparative Analysis of Edge Detection Techniques for Medical Images of Different Body Parts
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Metadata
Title
Weighted Fuzzy KNN Optimized by Simulated Annealing for Classification of Large Data: A New Approach to Skin Detection
Authors
Swati Aggarwal
Lehar Bhandari
Karan Kapoor
Jaswin Kaur
Copyright Year
2018
Publisher
Springer Singapore
Book
Data Science and Analytics

Print ISBN: 978-981-10-8526-0

Electronic ISBN: 978-981-10-8527-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-8527-7_14

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